Abstract
Previous Distance Density Clustering has shown some promising results for univariate time series datasets. However, due to the nature of time series data and from using Dynamic Time Warping algorithm as the distance measure; Distance Density Clustering is not an efficient heuristic with larger datasets. In this paper we propose a preprocessing step that could augment the algorithm to the parallel case, and speed up the Distance Density Clustering process considerably. We use time series sequence feature: peak numbers, to prune impossible matchings. By doing so, we are able to form preliminary feature clusters, and further clustering is applied within each feature cluster individually. This can narrow down the amount of time series distance computations, and make Distance Density Clustering scalable.
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Acknowledgment
This project has been supported in part by funding from the Division of Advanced Cyber infrastructure within the Directorate for Computer and Information Science and Engineering, the Division of Astronomical Sciences within the Directorate for Mathematical and Physical Sciences, and the Division of Atmospheric and Geospace Sciences within the Directorate for Geosciences, under NSF award #1443061. It was also supported in part by funding from the Heliophysics Living With a Star Science Program, under NASA award #NNX15AF39G.
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Ma, R., Filali Boubrahimi, S., Angryk, R. (2018). Time Series Distance Density Cluster with Statistical Preprocessing. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_28
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DOI: https://doi.org/10.1007/978-3-319-98539-8_28
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